Center For Systems Science And Engineering Research Articles

Correlation or Causation: Unraveling the Relationship between PM2.5 Air Pollution and COVID-19 Spread Across the United States

Numerous studies have examined the potential connection between air pollution, particularly PM2.5, and the incidence of COVID-19 cases during the pandemic. While several studies have demonstrated a strong correlation, caution is advised as correlation does not imply causation. To address this concern, our two-year observational study employs a comprehensive approach that utilizes a large sample size and draws on temporal and spatial data across the United States, surpassing the limitations of previous studies restricted to specific locations. Through rigorous correlation and regression analyses, we control for potential confounding factors. Air pollution data, a crucial component of our study, has been sourced from the United States Environmental Protection Agency (EPA). Additionally, COVID-19 case data is extracted from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, providing a robust and widely recognized dataset for our analyses. Notably, a significant spatial correlation exists between COVID-19 cases and population size (r=0.98, p-value <0.01), as confirmed by multivariate regression analysis, suggesting a confounding influence of population. It is crucial to emphasize that correlation does not automatically imply a direct cause-and-effect relationship. Moreover, to minimize the impact of population, we employ rates (COVID-19 cases/population of States), demonstrating that the rate of COVID-19 cases is independent of PM2.5 and population. Additionally, the rate of COVID-19 infection is not correlated with population density, implying the population's influence on infection is more likely due to probability rather than being a direct cause. In summary, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. In conclusion, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship.

A Comparative Study on Deep Learning Models for COVID-19 Forecast.

The COVID-19 pandemic has led to a global health crisis with significant morbidity, mortality, and socioeconomic disruptions. Understanding and predicting the dynamics of COVID-19 are crucial for public health interventions, resource allocation, and policy decisions. By developing accurate models, informed public health strategies can be devised, resource allocation can be optimized, and virus transmission can be reduced. Various mathematical and computational models have been developed to estimate transmission dynamics and forecast the pandemic's trajectories. However, the evolving nature of COVID-19 demands innovative approaches to enhance prediction accuracy. The machine learning technique, particularly the deep neural networks (DNNs), offers promising solutions by leveraging diverse data sources to improve prevalence predictions. In this study, three typical DNNs, including the Long Short-Term Memory (LSTM) network, Physics-informed Neural Network (PINN), and Deep Operator Network (DeepONet), are employed to model and forecast COVID-19 spread. The training and testing data used in this work are the global COVID-19 cases in the year of 2021 from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. A seven-day moving average as well as the normalization techniques are employed to stabilize the training of deep learning models. We systematically investigate the effect of the number of training data on the predicted accuracy as well as the capability of long-term forecast in each model. Based on the relative L2 errors between the predictions from deep learning models and the reference solutions, the DeepONet, which is capable of learning hidden physics given the training data, outperforms the other two approaches in all test cases, making it a reliable tool for accurate forecasting the dynamics of COVID-19.

Tackling the politicisation of COVID-19 data reporting through open access data sharing

Public health policies are only as good as the quality of the data on which they are based. Policy decisions that are so crucial to containing an emerging pathogen are challenged by the sparsity of data on which to optimise them. The earlier, and more completely, the data can be compiled, the better the robustness of risk estimates, forecasting, and modelling. Within 3 weeks of the announcement by WHO of an anomalous cluster of severe coronavirus cases in Wuhan, China, the Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE) launched the COVID-19 Dashboard.

PENGARUH IMPLEMENTASI KEBIJAKAN PROTOKOL KESEHATAN TERHADAP PENURUNAN KEJADIAN COVID-19

Background : One of the challenges in handling Covid-19 is that there are people who are against government policies. Some of these people still don't believe in the Covid-19 virus, distrust the information and reality about Covid-19 that the government provides, and not complying with government policies, including the implementation of health protocols. According to data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), Indonesia is in the top 20 list in the world. To be precise, it is in the 18th position, with the number of cases reaching 1,837,126 (as of June 24, 2021), and the death toll reaching 51,059 people. Methods: The research design is a descriptive quantitative survey with a cross sectional approach and the sampling technique used is accidental sampling and a sample of 71 respondents, which was then performed a chi-square statistical test, with a confidence degree of 95%. Results: The results of the statistical test show that there is a relationship between communication (p value 0.003), attitude (p value 0.001), and attitude of the implementer (p value 0.004) with a decrease in the number of cases of covid-19.

Exploring the role of country social and medical characteristics in patient level mortality in COVID-19 pandemic using Unsupervised Learning

This work aims to contribute to the field of COVID-19 pandemic analysis. In this research we applied a twofold analysis that focused initially on the country general social-economic and medical characteristics and on top of that in a second level exploring the correlations to the characteristics that affect COVID-19 patients’ mortality level. The approach has been applied to large datasets that include country level medical and the socio-economic data according to World Health Organization, the role of the cigarette consumption per capita using open datasets, and the cumulative data of the “COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University” for the total number of Cases, Deaths and Recovered. 101 countries including twenty-two (22) features are studied. We have also drilled in the country of Mexico datasets to show case the effectiveness of our approach. We show that our approach can achieve 96% overall accuracy based on the proposed combination approach of macro and micro features. Our approach outdoes previous study results that utilize machine learning to assist medical decision-making in COVID-19 prognosis. We conclude that country social economic and medical characteristics play important role to COVID-19 patients’ prognosis and their outcome.

COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients

ABSTRACT The Coronavirus Disease 2019 (COVID-19) pandemic has had social and clinical effects over the healthcare system. Globally, over 314.019.135 confirmed cases and 5.507.370 deaths have been recorded at 12 January 2022, based on the Dashboard updated by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The continuous rise in the number of asymptomatic, pre-symptomatic, and symptomatic patients has developed efficient and accessible models for prediction using Open Source Libraries and Cloud Services. This paper proposes different machine-learning algorithms to analyse the COVID-19 OpenData Resources from Mexico and Brazil, which represent the two major populations, economics and affected by disease countries in Latin America. This model uses only the COVID-19 patient’s geographical, social conditions, economics conditions, clinical risk factors, symptom reports, and demographic data to predict recovery and death. The model of Mexico has an accuracy of 93% and the perceived mean of recall and the precision (F1 Score) of 0.79 on the dataset used. On the other hand, the model of Brazil has an accuracy of 69% and F1 score of 0.75 on the dataset studied. The result considers data from patients under the age of 0 and 120 years. The contribution of the work is the application of Big Data technologies and Machine Learning algorithms using Open Resource Libraries and Amazon Web Services (AWS) with the vision to improve the clinical diagnosis, even infectious disease with mathematical approaches.

Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA.

Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University’s (JHU’s) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU’s data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90–2.69 [95%CI: 1.38–4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51–0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols.

Кратковременные и долговременные последствия коронавирусной инфекции для организма (обзор)

To date, according to the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, there have been 250 million cases of the disease in the world, as well as 5.05 million deaths, which is almost equal to the population of the second largest city in Russia—Saint Petersburg. According to the current data, the leading countries in terms of patients infected and the number of deaths are the United States (46.4 million cases), India (34.4 million cases), Brazil (21.9 million), UK (9.3 million) and Russia (8.65). These statistics, among other factors, are attributed to high population density. The new coronavirus infection has led to a pandemic and has had physical, mental, psychological, social and economic consequences for the world population. To date, there is evidence of adverse effects of similar diseases caused by coronaviruses, namely, Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The obtained data suggest that COVID-19 has a negative impact on various systems of the human body: respiratory, cardiovascular, immune, and nervous. Possible complications include encephalitis, toxic encephalopathy, acute demyelinating lesions. COVID-19 can also intensify pre-existing neurological conditions. The paper discusses short-term adverse health effects as well as the risk of potential long-term negative effects associated with COVID-19.

КОВИДНЫЙ НИГИЛИЗМ В УСЛОВИЯХ БОРЬБЫ С ПАНДЕМИЕЙ COVID-19

Significance. Emergence of such phenomenon as "COVID nihilism" and "COVID dissidents" can’t stop the COVID-19 pandemic. The purpose of the study is to review factors determining COVID nihilism in the fight against the COVID-19 pandemic in the world and in Russia. Material and methods. The information base of the study included publications in periodicals and the Internet, statistical materials of the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), YandexCoronavirus. Statistics and methods: general logical, analytical, statistical analysis and empirical research. Results. The main reasons for COVID nihilism and opposition to vaccine and mask mandates included imposition of freedom, independence of one's own body, belief in conspiracy theories, and concern about the side effects of masks and vaccines. The Internet activity to sabotage anti-COVID measures in the Russian Federation is rather pronounced, yet it failed to result in any riots and pogroms, unlike the EU countries. The share of people vaccinated against COVID-19 in the Russian Federation is twice lower compared to China, for example, due to citizens' concerns about the need for vaccination and fear due to the low level of virology literacy among the population. The main barriers to stop the pandemic are disbelief that the pathogen SARS-CoV-2 exists, disbelief that masks protect against infection and microdroplets containing viral particles, improper face mask wearing (inside out), violation of social distance, misinformation on the virus prevention and spread, anti-vaccine attitude due to misunderstanding of the process of vaccine development and action, social instability and mistrust in the authorities. Consequences of the ongoing pandemic include burden on healthcare system, excess mortality and post-COVID complications affecting quality of life of the entire population and duration of a healthy life. Under-examination and care deficiency, shortage of personnel, and care facility repurposing result in higher mortality from causes not associated with the COVID-19 infection.

Quo Vadis, COVID-19?

Quo Vadis, COVID-19?

PNEUMATOCELE FORMATION DUE TO SARS-COV2: A CASE REPORT

PNEUMATOCELE FORMATION DUE TO SARS-COV2: A CASE REPORT

The challenging of HIV care 1 year after of coronavirus disease 2019 pandemic: results from a Brazilian cohort.

The challenging of HIV care 1 year after of coronavirus disease 2019 pandemic: results from a Brazilian cohort.

SARS-CoV-2 routes of transmission and recommendations for preventing acquisition: joint British Infection Association (BIA), Healthcare Infection Society (HIS), Infection Prevention Society (IPS) and Royal College of Pathologists (RCPath) guidance

SARS-CoV-2 routes of transmission and recommendations for preventing acquisition: joint British Infection Association (BIA), Healthcare Infection Society (HIS), Infection Prevention Society (IPS) and Royal College of Pathologists (RCPath) guidance

Predicting and Analysing the Behaviour of COVID-19

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

Influence of age and gender on the epidemic of COVID-19 : Evidence from 177countries and territories-an exploratory, ecological study.

BackgroundThe pandemic of coronavirus disease 2019 (COVID-19) is spreading worldwide with an uncertain ultimate impact on every aspect of human society. Recognizing the groups with higher susceptibility and fatality are in urgent need.MethodsWe retrieved the total number of confirmed incident and death cases of COVID-19 in 177 countries/territories from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Data of age and gender composition were collected from the United Nations Department of Economic and Social Affairs. Spearman’s correlation analysis was used to explore the association between the composition of age and gender and the incidence rate (IRC), case fatality rate (CFRC), and mortality rate of COVID-19 (MRC). Multiple comparisons were adjusted by the Bonferroni method, and the threshold p-value was set as p < 0.01. Software SPSS 23.0, ArcGIS 10.6, and GraphPad Prism 8.0 were used to generate our results.ResultsMedian age was positively correlated to IRC, CFRC and MRC (rs = 0.60; rs = 0.27; rs = 0.61, p < 0.0001 for all). The age-dependent correlation between people over 65 years of age with IRC was higher in females, while the correlation between age distribution and CFRC as well as MRC was higher in males (p < 0.0001 for all). Besides, we found the age-gender-dependent differences were correlated to IRC in places with high income and associated with CFRC in non-high income countries/territories.ConclusionThe correlation between the composition of age and gender and the epidemic characteristics of COVID-19 confirmed previous points that females are more susceptible to COVID-19. The results remind us that more attention should be paid to male patients, particularly those over 65 years old for enhanced clinical management.Supplementary InformationThe online version of this article (10.1007/s00508-021-01816-z) contains supplementary material, which is available to authorized users.

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: \u2013 evidence from Baidu index

BackgroundNew coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.MethodsWe collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.ResultsDaily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever.ConclusionThe searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19.

COVID-19 is a novel coronavirus affecting all the world since December last year. Up to date, the spread of the outbreak continues to complicate our lives, and therefore, several research efforts from many scientific areas are proposed. Among them, mathematical models are an excellent way to understand and predict the epidemic outbreaks evolution to some extent. Due to the COVID-19 may be modeled as a non-Markovian process that follows power-law scaling features, we present a fractional-order SIRD (Susceptible-Infected-Recovered-Dead) model based on the Caputo derivative for incorporating the memory effects (long and short) in the outbreak progress. Additionally, we analyze the experimental time series of 23 countries using fractal formalism. Like previous works, we identify that the COVID-19 evolution shows various power-law exponents (no just a single one) and share some universality among geographical regions. Hence, we incorporate numerous memory indexes in the proposed model, i.e., distinct fractional-orders defined by a time-dependent function that permits us to set specific memory contributions during the evolution. This allows controlling the memory effects of more early states, e.g., before and after a quarantine decree, which could be less relevant than the contribution of more recent ones on the current state of the SIRD system. We also prove our model with Italy’s real data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.

Navigating the COVID-19 pandemic: Initial impacts and responses of the Organ Procurement and Transplantation Network in the United States.

COVID‐19 has been sweeping the globe, hitting the United States particularly hard with a state of emergency declared on March 13, 2020. Transplant hospitals have taken various precautions to protect patients from potential exposure. OPTN donor, candidate, and transplant data were analyzed from January 5, 2020 to September 5, 2020. The number of new waiting list registrations decreased, with the Northeast seeing over a 50% decrease from the week of 3/8 versus the week of 4/5. The national transplant system saw near cessation of living donor transplantation (−90%) from the week of 3/8 to the week of 4/5. Similarly, deceased donor kidney transplant volume dropped from 367 to 202 (−45%), and other organs saw similar decreases: lung (−70%), heart (−43%), and liver (−37%). Deceased donors recovered dropped from 260 to 163 (−45%) from 3/8 compared to 4/5, including a 67% decrease for lungs recovered. The magnitude of this decrease varied by geographic area, with the largest percent change (−67%) in the Northeast. Despite the pandemic, discard rates across organ has remained stable. Although the COVID‐19 pandemic continues to evolve, OPTN data show recent evidence of stabilization, an indication that an early recovery of the number of living and deceased donors and transplants has ensued.

Monitoring Hospitalized Dialysis Patients With COVID-19: Repurposing Baby Monitors for Patient and Staff Safety

Monitoring Hospitalized Dialysis Patients With COVID-19: Repurposing Baby Monitors for Patient and Staff Safety

Performance Estimation of Machine Learning Algorithms in the Factor Analysis of COVID-19 Dataset

Novel Coronavirus Disease (COVID-19) is a communicable disease that originated during December 2019, when China officially informed the World Health Organization (WHO) regarding the constellation of cases of the disease in the city of Wuhan. Subsequently, the disease started spreading to the rest of the world. Until this point in time, no specific vaccine or medicine is available for the prevention and cure of the disease. Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early detection of this viral disease. The present report describes the use of machine learning algorithms [Linear and Logistic Regression, Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and SVM with Grid Search] for the prediction and classification in relation to COVID-19. The data used for experimentation was the COVID-19 dataset acquired from the Center for Systems Science and Engineering (CSSE), Johns Hopkins University (JHU). The assimilated results indicated that the risk period for the patients is 12–14 days, beyond which the probability of survival of the patient may increase. In addition, it was also indicated that the probability of death in COVID cases increases with age. The death probability was found to be higher in males as compared to females. SVM with Grid search methods demonstrated the highest accuracy of approximately 95%, followed by the decision tree algorithm with an accuracy of approximately 94%. The present study and analysis pave a way in the direction of attribute correlation, estimation of survival days, and the prediction of death probability. The findings of the present study clearly indicate that machine learning algorithms have strong capabilities of prediction and classification in relation to COVID-19 as well.